Research on traffic sign recognition method based on multi-scale convolution neural network

Tiancheng Wei; Xiaofeng Chen; Yuanliang Yin

doi:10.1051/jnwpu/20213940891

All issues

Volume 39 / No 4 (August 2021)

JNWPU, 39 4 (2021) 891-900

References

Open Access

Issue		JNWPU Volume 39, Number 4, August 2021


Page(s)		891 - 900
DOI		https://doi.org/10.1051/jnwpu/20213940891
Published online		23 September 2021

Zhang J, Wang F, Wang K, et al. Data-driven intelligent transportation systems: a survey[J]. IEEE Trans on Intelligent Transpor-tation Systems, 2011, 12(4): 1624–1639 [Article] [Google Scholar]
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Fan Yanjun, Zhang Weigong. Traffic sign detection and classification for advanced driver assistant systems[C]//International Conference on Fuzzy Systems and Knowledge Discovery(FSKD), Zhangjiajie, 2015: 1335–1339 [Google Scholar]
Ge Xia, Yu Fengqin, Chen Ying. Traffic sign recognition based on blocking self-adaptation fusion features[J]. Computer Engineering and Applications, 2017, 53(3): 188–192 [Article] (in Chinese) [Google Scholar]
Sha Sha, Xiao Xuegang. Traffic sign detection method with multi-channel integration[J]. Computer Engineering, 2009, 35(6): 202–204 [Article] (in Chinese) [NASA ADS] [Google Scholar]
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Aziz S, Mohamed E A, Youssef F. Traffic sign recognition based on multi-feature fusion and ELM classifier[J]. Procedia Computer Science, 2018, 127(1): 146–153 [Article] [Google Scholar]
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Xiong C, Cong W, Ma W, et al. A traffic sign detection algorithm based on deep convolutional neural network[C]//IEEE International Conference on Signal and Image Processing, 2016 [Google Scholar]
Ren Shaoqing, He Kaiming, Girshick Ross, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149 [Article] [Google Scholar]
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans on Patcern Analysis and Machrne Interlligence, 2015, 39(4): 640–651 [Article] [Google Scholar]
Li Kai, Han Bing, Zhang Jingtao. Traffic sign detection and recognition based on oonditional random field and multi-scale convolutional neural network[J]. Journal of Computer Applications, 2018, 38(suppl 2): 270–275 [Article] (in Chinese) [Google Scholar]
Houben S, Stallkamp J, Salmen J, et al. Detection of traffic signs in real-world images: the German traffic sign detection benchmark[C]//The 2013 International Joint Conference on Neural Networks, Dallas, TX, 2013: 1–8 [Google Scholar]
Zhu Z, Liang D, Zhang S, et al. Traffic-sign detection and classification in the wild[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016 [Google Scholar]

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[1] Zhang J, Wang F, Wang K, et al. Data-driven intelligent transportation systems: a survey[J]. IEEE Trans on Intelligent Transpor-tation Systems, 2011, 12(4): 1624–1639 [Article] [Google Scholar]

[2] Seo Y, Lee J, Zhang W, et al. Recognition of highway workzones for reliable autonomous driving[J]. IEEE Trans on Intelligent Transportation Systems, 2015, 16(2): 708–718 [Article] [Google Scholar]

[3] Fan Yanjun, Zhang Weigong. Traffic sign detection and classification for advanced driver assistant systems[C]//International Conference on Fuzzy Systems and Knowledge Discovery(FSKD), Zhangjiajie, 2015: 1335–1339 [Google Scholar]

[4] Ge Xia, Yu Fengqin, Chen Ying. Traffic sign recognition based on blocking self-adaptation fusion features[J]. Computer Engineering and Applications, 2017, 53(3): 188–192 [Article] (in Chinese) [Google Scholar]

[5] Sha Sha, Xiao Xuegang. Traffic sign detection method with multi-channel integration[J]. Computer Engineering, 2009, 35(6): 202–204 [Article] (in Chinese) [NASA ADS] [Google Scholar]

[6] Madani A, Yusof R. Traffic sign recognition based on color, shape, and pictogram classification using support vector machines[J]. Neural Computing & Applications, 2017, 36(5): 1–11 [Article] [Google Scholar]

[7] Aziz S, Mohamed E A, Youssef F. Traffic sign recognition based on multi-feature fusion and ELM classifier[J]. Procedia Computer Science, 2018, 127(1): 146–153 [Article] [Google Scholar]

[8] Boumediene M, Cudel C, Basset M, et al. Triangular traffic signs detection based on RSLD algorithm[J]. Machine Vision and Applications, 2013, 24(8): 1721–1732 [Article] [Google Scholar]

[9] Lillo-Castellano J M, Jiménez I, Pozuelo C, et al. Traffic sign segmentation and classify-cation using statistical learning methods[J]. Neurocomputing, 2015, 153: 286–299 [Article] [CrossRef] [Google Scholar]

[10] Girshick R. Fast R-CNN[J/OL]. (2015-09-27)[2020-09-01]. https://arxiv.org/pdf/1504.08083.pdf [Google Scholar]

[11] Cheng P, Liu W, Zhang Y, et al. LOCO: local context based faster R-CNN for small traffic sign detection[C]//International Conference on Multimedia Modeling, Cham, 2018 [Google Scholar]

[12] Xiong C, Cong W, Ma W, et al. A traffic sign detection algorithm based on deep convolutional neural network[C]//IEEE International Conference on Signal and Image Processing, 2016 [Google Scholar]

[13] Ren Shaoqing, He Kaiming, Girshick Ross, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149 [Article] [Google Scholar]

[14] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans on Patcern Analysis and Machrne Interlligence, 2015, 39(4): 640–651 [Article] [Google Scholar]

[15] Li Kai, Han Bing, Zhang Jingtao. Traffic sign detection and recognition based on oonditional random field and multi-scale convolutional neural network[J]. Journal of Computer Applications, 2018, 38(suppl 2): 270–275 [Article] (in Chinese) [Google Scholar]

[16] Houben S, Stallkamp J, Salmen J, et al. Detection of traffic signs in real-world images: the German traffic sign detection benchmark[C]//The 2013 International Joint Conference on Neural Networks, Dallas, TX, 2013: 1–8 [Google Scholar]

[17] Zhu Z, Liang D, Zhang S, et al. Traffic-sign detection and classification in the wild[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016 [Google Scholar]